Please wait a minute...
Journal of Integrative Agriculture  2013, Vol. 12 Issue (1): 110-117    DOI: 10.1016/S2095-3119(13)60211-7
ANIMAL SCIENCE · VETERINARY SCIENCE Advanced Online Publication | Current Issue | Archive | Adv Search |
Fine Mapping QTLs Affecting Milk Production Traits on BTA6 in Chinese Holstein with SNP Markers
 LIU Rui, SUN Dong-xiao, WANG Ya-chun, YU Ying, ZHANG Yi, CHEN Hui-yong, ZHANG Qin, ZHANG Sheng-li , ZHANG Yuan
1.College of Animal Science and Technology/Key Laboratory of Animal Genetics and Breeding, Ministry of Agriculture/National Engineering Laboratory of Animal Genetics/China Agricultural University, Beijing 100193, P.R.China
2.Sichuan Animal Science Academy, Chengdu 610066, P.R.China
Download:  PDF in ScienceDirect  
Export:  BibTeX | EndNote (RIS)      
摘要  Our previous studies demonstrated that the region around markers BMS470 and BMS1242 on BTA6 showed a linkage to 305-d milk yield and composition traits in the Chinese Holstein population. We herein focused on such narrow region to fine map milk production QTLs with 15 SNPs across 25 Mb with each SNP in 1 Mb within most regions in a Chinese Holstein population with daughter design. 1 449 Holstein cows and 11 sires were genotyped for such SNPs by using TaqMan probe and RFLP assays. Multipoint linkage analysis across family revealed a QTL affecting milk yield between PPARGC1A C4075T and SLC34A2 T1713C. Meanwhile, within family analysis found three milk yield QTLs (two in CR T60984131G-CEP135 C501T and one in PDLIM5 A106C-OPN T3907, a fat yield QTL in UGDH T1670C-CR T60984131G region, and two protein yield QTLs in TBC1D1 G501C-UGDH T1670C and PPARGC1A C4075T-SLC34A2 T1713C, respectively. Associations between aforementioned significant SNP markers and milk production traits were further implemented. We found significant associations of PPARGC1A C4075T, SLC34A2 T1713C with milk yield (P<0.05, P<0.01, P<0.01), UGDH T1670C, and CR T60984131G with fat yield (P<0.01, P<0.01), and PPARGC1A C4075T, SLC34A2 T1713C, UGDH T1670C and OPN T3907 with protein yield (P<0.01, P<0.01, P<0.01, P<0.01). Our findings implied that QTLs affecting milk production traits on BTA6 were pleictropism or multigenic effect and PPARGC1A and OPN may be the causal mutations behind milk production QTLs on BTA6 in the Chinese Holstein population.

Abstract  Our previous studies demonstrated that the region around markers BMS470 and BMS1242 on BTA6 showed a linkage to 305-d milk yield and composition traits in the Chinese Holstein population. We herein focused on such narrow region to fine map milk production QTLs with 15 SNPs across 25 Mb with each SNP in 1 Mb within most regions in a Chinese Holstein population with daughter design. 1 449 Holstein cows and 11 sires were genotyped for such SNPs by using TaqMan probe and RFLP assays. Multipoint linkage analysis across family revealed a QTL affecting milk yield between PPARGC1A C4075T and SLC34A2 T1713C. Meanwhile, within family analysis found three milk yield QTLs (two in CR T60984131G-CEP135 C501T and one in PDLIM5 A106C-OPN T3907, a fat yield QTL in UGDH T1670C-CR T60984131G region, and two protein yield QTLs in TBC1D1 G501C-UGDH T1670C and PPARGC1A C4075T-SLC34A2 T1713C, respectively. Associations between aforementioned significant SNP markers and milk production traits were further implemented. We found significant associations of PPARGC1A C4075T, SLC34A2 T1713C with milk yield (P<0.05, P<0.01, P<0.01), UGDH T1670C, and CR T60984131G with fat yield (P<0.01, P<0.01), and PPARGC1A C4075T, SLC34A2 T1713C, UGDH T1670C and OPN T3907 with protein yield (P<0.01, P<0.01, P<0.01, P<0.01). Our findings implied that QTLs affecting milk production traits on BTA6 were pleictropism or multigenic effect and PPARGC1A and OPN may be the causal mutations behind milk production QTLs on BTA6 in the Chinese Holstein population.
Keywords:  fine mapping       milk production trait       SNP       BTA6       Chinese Holstein  
Received: 08 June 2011   Accepted:
Fund: 

This work was supported by the National 948 Project of China (2006-G48), the National Key Technologies R&D Program of China (2006BAD04A01), the Key Development of New Transgenic Breeds Program of China (2009ZX08009-156B), and the National Natural Science Foundation of China (31072016).

Corresponding Authors:  Correspondence ZHANG Yuan, Tel/Fax: +86-10-62733687, E-mail: changy@cau.edu.cn   
About author:  SUN Dong-xiao, Tel: +86-10-62734653, E-mail: sundx@cau.edu.cn

Cite this article: 

LIU Rui, SUN Dong-xiao, WANG Ya-chun, YU Ying, ZHANG Yi, CHEN Hui-yong, ZHANG Qin, ZHANG Sheng-li , ZHANG Yuan. 2013. Fine Mapping QTLs Affecting Milk Production Traits on BTA6 in Chinese Holstein with SNP Markers. Journal of Integrative Agriculture, 12(1): 110-117.

1.College of Animal Science and Technology/Key Laboratory of Animal Genetics and Breeding, Ministry of Agriculture/National Engineering Laboratory of Animal Genetics/China Agricultural University, Beijing 100193, P.R.China

2.Sichuan Animal Science Academy, Chengdu 610066, P.R.China[1]Ashwell M S, Schnabel R S, Sonstegard T S, van TassellCP. 2002. Fine-mapping of QTL affecting protein percentand fat percent on BTA6 in a popular U.S. Holsteinfamily. In: Proceedings of the 7th World Congress onGeneti cs Appl ied to Livestock Produc t ion.Montpellier, France.

[2]Chen H Y, Zhang Q, Yin C C, Wang C K, Gong W J, Mei G.2006. Detection of quantitative trait loci affecting milkproduction traits on bovine chromosome six in ChineseHolstein population by the daughter design. Journalof Dairy Science, 89, 782-790

[3]Daetwyler H D, Schenkel FS, Sargolzaei M, Robinson J A2008. A genome scan to detect quantitative trait loci foreconomically important traits in Holstein cattle usingtwo methods and a dense single nucleot idepolymorphism map. Journal of Dairy Science, 91, 3225-3236

[4]Everts-van der Wind A, Kata S R, Band M R, Rebeiz M,Larkin D M, Everts R E, Green, C A, Liu L, Natarajan S,Goldammer T, et al. 2004. A 1463 gene cattle-humancomparative map with anchor points defined by humangenome sequence coordinates. Genome Research, 14,1424-1437

[5]Ewing B, Green P. 1998. Base-calling of automatedsequencer traces using phred. II. Error probabilities.Genome Research, 8, 186-194

[6]Ewing B, Hillier L, Wendl M C, Green P. 1998. Base-callingof automated sequencer traces using phred. I. Accuracyassessment. Genome Research, 8, 175-185

[7]Freyer G, Kühn C, Weikard R, Zhang Q, Mayer M,Hoeschele I. 2002. Multiple QTL on chromosome six indairy cattle affecting yield and content traits. Journalof Animal Breeding Genetics, 119, 69-82

[8]Freyer G, Sorensen P, Kuhn C, Weikard R, Hoeschele I.2003. Search for pleiotropic QTL on chromosome BTA6affecting yield traits for milk production. Journal ofDairy Science, 86, 999-1008

[9]Gordon D, Abajian C, Green P. 1998. Consed: a graphicaltool for sequence finishing. Genome Research, 8, 195-202

[10]Heath S C. 1997. Markov chain Monte Carlo segregationand linkage analysis for oligogenic models. AmericanJournal of Human Genetics, 61, 748-760

[11]Ihara N, Takasuga A, Mizoshita K, Takeda H, Sugimoto M,Mizoguchi Y, Hirano T, Itoh T, Watanabe T, Reed K M,et al. 2004. A comprehensive genetic map of the cattlegenome based on 3802 microsatellites. GenomeResearch, 14, 1987-1998

[12]Khatib H, Zaitoun I, Wiebelhaus-Finger J, Chang Y M,Rosa G J. 2007. The association of bovine ppargc1aand opn genes with milk composition in twoindependent Holstein cattle populations. Journal ofDairy Science, 90, 2966-2670

[13]Kolbehdari D, Wang Z, Grant J R, Murdoch B, Prasad A.2009. A whole genome scan to map QTL for milkproduction traits and somatic cell score in CanadianHolstein bulls. Journal of Animal Breeding andGenetics, 126, 216-227

[14]Leonard S, Khatib H, Schutzkus V, Chang Y M, MalteccaC. 2005. Effects of the osteopontin gene variants onmilk production traits in dairy cattle. Journal DairyScience, 88, 4083-4086

[15]Jiang L, Liu J F, Sun D X, Ma P P, Ding X D, Yu Y, Zhang Q.2010. Genome wide association studies for milkproduction traits in chinese holstein population.PLosONE, 5, e13661.

[16]Mei G, Yin C C, Ding X D, Zhang Q. 2009. Fine mappingquantitative trait loci affecting milk production traitson bovine chromosome 6 in a Chinese Holsteinpopulation. Journal of Genetics and Genomics, 36, 653-660

[17]Montgomery K T, Iartchouck O, Li L, Loomis S, Obourn V,Kucherlapati R. 2008. PolyPhred analysis software formutation detection from fluorescence-based sequencedata. In: Current Protocol in Human Genetics, doi: 10.1002/0471142905.hg0716s59Nickerson D A, Tobe V O, Taylor S L. 1997. PolyPhred:automating the detection and genotyping of singlenucleotide substitutions using fluorescencebasedresequencing. Nucleic Acids Research, 25, 2745-2751

[18]Olsen H G, Lien S, GautierM, Nilsen H, Roseth A, Berg P R,Sundsaasen K K, Svendsen M, Meuwissen T H. 2005.Mapping of a milk production QTL to a 420 kb regionon bovine chromosome 6. Genetics, 169, 275-283

[19]Olsen H G, Lien S, SvendsenM, Nilsen H, RosethA, AaslandOpsal M, Meuwissen T H. 2004. Fine mapping of milkproduction QTL on BTA6 by combined linkage andlinkage disequilibrium analysis. Journal of DairyScience, 87, 690-698

[20]Schibler L, RoigA, Mahé M F, Save J C, Gautier M, TaouritS, Boichard D, Eggen A, Cribiu E P. 2004. A firstgeneration bovine BAC-based physical map. GeneticsSelection Evolotion, 36, 105-122

[21]Schnabel R D, Kim J J, Ashwell M S, Sonstegard T S, vanTassell C P, Connor E E, Taylor J F. 2005. Fine-mappingmilk production quantitative trait loci on BTA6: analysisof the bovine osteopontin gene. Proceedings of theNational Academy of Sciences of the United States ofAmerica, 102, 6896-6901

[22]Spelman R J, Coppieters W, Karim L, van Arendonk J A,Bovenhuis H. 1996. Quantitative trait loci analysis forfive milk production traits on chromosome six in theDutch Holstein-Friesian population. Genetics, 144,1799-1808

[23]Teng X H. 2006. Studies of adjustment factors forstandardizing milking records of Chinese Holstein. MScthesis, China Agricultural University, China. (in Chinese)

[24]Weikard R, Goldammer T, Laurent P, Womack J E, Kuehn C.2006. A gene-based high-resolution comparativeradiation hybrid map as a framework for genomesequence assembly of a bovine chromosome 6 regionassociated with QTL for growth, body composition,and milk performance traits. BMC Genomics, 7, 53.

[25]Weikard R, Kühn C, Goldammer T, Freyer G, Schwerin M.2005. The bovine PPARGC1A gene: Molecularcharacterization and association of an SNP withvariation of milk fat synthesis. Physiological Genomics,21, 1-13

[26]Weikard R, Kühn C, Goldammer T, Laurent P, Womack J E,Schwerin M. 2002. A high resolution comparative mapfor a bovine chromosome 6 (BTA6) region containingQTL for production, health and conformation traits. In:7th World Congress on Genetics Applied to LivestockProduction. Montpellier, France.

[27]Wiener P, Maclean I, Williams J L, Woolliams J A. 2000.Testing for the presence of previously identified QTLfor milk production traits in new populations. AnimalGenetics, 31, 385-95
[1] Yapeng Zhang, Wentao Cai, Qi Zhang, Qian Li, Yahui Wang, Ruiqi Peng, Haiqi Yin, Xin Hu, Zezhao Wang, Bo Zhu, Xue Gao, Yan Chen, Huijiang Gao, Lingyang Xu, Junya Li, Lupei Zha. Integrated analyses of genomic and transcriptomic data reveal candidate variants associated with carcass traits in Huaxi cattle[J]. >Journal of Integrative Agriculture, 2025, 24(8): 3169-3184.
[2] Jianqi Zeng, Dehui Zhao, Li Yang, Yufeng Yang, Dan Liu, Yubing Tian, Fengju Wang, Shuanghe Cao, Xianchun Xia, Zhonghu He, Yong Zhang. Fine mapping and candidate gene analysis of a major QTL for grain length on chromosome 5BS in bread wheat[J]. >Journal of Integrative Agriculture, 2025, 24(7): 2465-2474.
[3] Wei Liu, Xueling Huang, Meng Ju, Mudi Sun, Zhimin Du, Zhensheng Kang, Jie Zhao. Molecular evidence of the west-to-east dispersal of Puccinia striiformis f. sp. tritici in central Shaanxi and the migration of the inoculum from Gansu[J]. >Journal of Integrative Agriculture, 2025, 24(6): 2251-2265.
[4] Feifan Wu, Luoyang Ding, Shane K Maloney, Dominique Blache, Mengzhi Wang. Temperament and production in ruminants: The microbiome as one of the factors that affect temperament[J]. >Journal of Integrative Agriculture, 2025, 24(11): 4111-4126.
[5] Yong Yang, Rong Fan, Xuejun Zhang, Meihua Li, Yongbing Zhang, Hongping Yi, Manrui Ma, Yun Yang, Bin Liu, Xingwang Liu, Huazhong Ren. Mutation in CmGhc1 confers the white hypocotyl phenotype in melon (Cucumis melo L.)[J]. >Journal of Integrative Agriculture, 2025, 24(11): 4242-4254.
[6] Guanghui Chen, Li Sheng, Lijun Wu, Liang Yin, Shuangling Li, Hongfeng Wang, Xiao Jiang, Heng Wang, Yanmao Shi, Fudong Zhan, Xiaoyuan Chi, Chunjuan Qu, Yan Ren, Mei Yuan. Identification of novel QTLs for resistance to late leaf spot in peanut by SNP array and QTL-seq analyses[J]. >Journal of Integrative Agriculture, 2025, 24(10): 3772-3788.
[7] Yingzhen Wang, Ying Wu, Xinlei Wang, Wangmei Ren, Qinyao Chen, Sijia Zhang, Feng Zhang, Yunzhi Lin, Junyang Yue, Yongsheng Liu.

Genome wide association analysis identifies candidate genes for fruit quality and yield in Actinidia eriantha  [J]. >Journal of Integrative Agriculture, 2024, 23(6): 1929-1939.

[8] Liping Song, Xia Li, Liguang Tang, Chuying Yu, Bincai Wang, Changbin Gao, Yanfeng Xie, Xueli Zhang, Junliang Wang, Chufa Lin, Aihua Wang.

Fine mapping and cloning of the sterility gene Bra2Ms in non-heading Chinese cabbage (Brassica rapa ssp. chinensis) [J]. >Journal of Integrative Agriculture, 2024, 23(4): 1195-1204.

[9] Wenting Li, Chaoqun Gao, Zhao Cai, Sensen Yan, Yanru Lei, Mengya Wei, Guirong Sun, Yadong Tian, Kejun Wang, Xiangtao Kang.

Assessing the conservation impact of Chinese indigenous chicken populations between ex-situ and in-situ using genome-wide SNPs [J]. >Journal of Integrative Agriculture, 2024, 23(3): 975-987.

[10] Yang Yang, Hongfei Li, Changhao Liang, Donghai He, Hang Zhao, Hongbo Jiang, Jinjun Wang. Neuropeptide signaling systems are involved in regulating thermal tolerance in the oriental fruit fly[J]. >Journal of Integrative Agriculture, 2024, 23(12): 4147-4160.
[11] YAN Sheng-nan, YU Zhao-yu, GAO Wei, WANG Xu-yang, CAO Jia-jia, LU Jie, MA Chuan-xi, CHANG Cheng, ZHANG Hai-ping. Dissecting the genetic basis of grain color and pre-harvest sprouting resistance in common wheat by association analysis[J]. >Journal of Integrative Agriculture, 2023, 22(9): 2617-2631.
[12] LIU Dan, ZHAO De-hui, ZENG Jian-qi, Rabiu Sani SHAWAI, TONG Jing-yang, LI Ming, LI Fa-ji, ZHOU Shuo, HU Wen-li, XIA Xian-chun, TIAN Yu-bing, ZHU Qian, WANG Chun-ping, WANG De-sen, HE Zhong-hu, LIU Jin-dong, ZHANG Yong. Identification of genetic loci for grain yield‑related traits in the wheat population Zhongmai 578/Jimai 22[J]. >Journal of Integrative Agriculture, 2023, 22(7): 1985-1999.
[13] LI Jia-chuang, LI Jiao-jiao, ZHAO Li, ZHAO Ji-xin, WU Jun, CHEN Xin-hong, ZHANG Li-yu, DONG Pu-hui, WANG Li-ming, ZHAO De-hui, WANG Chun-ping, PANG Yu-hui. Rapid identification of Psathyrostachys huashanica Keng chromosomes in wheat background based on ND-FISH and SNP array methods[J]. >Journal of Integrative Agriculture, 2023, 22(10): 2934-2948.
[14] WANG Xiao-dong, CAI Ying, PANG Cheng-ke, ZHAO Xiao-zhen, SHI Rui, LIU Hong-fang, CHEN Feng, ZHANG Wei, FU San-xiong, HU Mao-long, HUA Wei, ZHENG Ming, ZHANG Jie-fu. BnaSD.C3 is a novel major quantitative trait locus affecting semi-dwarf architecture in Brassica napus L.[J]. >Journal of Integrative Agriculture, 2023, 22(10): 2981-2992.
[15] ZHANG Chuan, WU Jiu-yun, CUI Li-wen, FANG Jing-gui. Mining of candidate genes for grape berry cracking using a genome-wide association study[J]. >Journal of Integrative Agriculture, 2022, 21(8): 2291-2304.
No Suggested Reading articles found!